Know your Microbe Foes: The Role of Surveillance in Combatting Antimicrobial Resistance

Antibiotic-resistant organisms (AROs) are difficult and costly to treat, associated with high mortality rates, and are on the rise. In the United States, there is limited tracking of AROs, which can contribute to transmission and inhibit infection prevention interventions. Surveillance is limited by a lack of standardized methods for colonization screening and limited communication regarding patient ARO-status between healthcare settings. Some regional surveillance and reporting efforts are in place for extensively-resistant AROs such as carbapenem-resistant Enterobacterales (CRE), but need to be further expanded nationwide and to include other AROs such as extended-spectrum β-lactamase (ESBL) producing organisms. Increased surveillance of ARO infections and colonization will inform future targeted intervention and infection prevention strategies.

Gut Microbiota Features on Nursing Home Admission Are Associated With Subsequent Acquisition of Antibiotic-resistant Organism Colonization

Nursing home (NH) patients often acquire colonization with antibiotic-resistant organisms (AROs). We show that patients exposed to broad-spectrum antibiotics during previous hospitalizations have elevated enterococcal relative abundances on NH admission and higher risk of subsequent ARO acquisition. Our findings suggest that interventions preventing ARO spread should extend beyond NH doors.

Application of Combined Genomic and Transfer Analyses to Identify Factors Mediating Regional Spread of Antibiotic-resistant Bacterial Lineages

BACKGROUND

Patients entering nursing facilities (NFs) are frequently colonized with antibiotic-resistant organisms (AROs). To understand the determinants of ARO colonization on NF admission, we applied whole-genome sequencing to track the spread of 4 ARO species across regional NFs and evaluated patient-level characteristics and transfer acute care hospitals (ACHs) as risk factors for colonization.

METHODS

Patients from 6 NFs (n = 584) were surveyed for methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus faecalis/faecium (VREfc/VREfm), and ciprofloxacin-resistant Escherichia coli (CipREc) colonization. Genomic analysis was performed to quantify ARO spread between NFs and compared to patient-transfer networks. The association between admission colonization and patient-level variables and recent ACH exposures was examined.

RESULTS

The majority of ARO isolates belonged to major healthcare-associated lineages: MRSA (sequence type [ST] 5); VREfc (ST6); CipREc (ST131), and VREfm (clade A). While the genomic similarity of strains between NF pairs was positively associated with overlap in their feeder ACHs (P < .05 for MRSA, VREfc, and CipREc), limited phylogenetic clustering by either ACH or NF supported regional endemicity. Significant predictors for ARO colonization on NF admission included lower functional status and recent exposure to glycopeptides (adjusted odds ratio [aOR], > 2 for MRSA and VREfc/VREfm) or third-/fourth-generation cephalosporins (aOR, > 2 for MRSA and VREfm). Transfer from specific ACHs was an independent risk factor for only 1 ARO/ACH pair (VREfm/ACH19: aOR, 2.48).

CONCLUSIONS

In this region, healthcare-associated ARO lineages are endemic among connected NFs and ACHs, making patient characteristics more informative of NF admission colonization risk than exposure to specific ACHs.

Using Genetic Distance from Archived Samples for the Prediction of Antibiotic Resistance in Escherichia coli

The rising rates of antibiotic resistance increasingly compromise empirical treatment. Knowing the antibiotic susceptibility of a pathogen's close genetic relative(s) may improve empirical antibiotic selection. Using genomic and phenotypic data for Escherichia coli isolates from three separate clinically derived databases, we evaluated multiple genomic methods and statistical models for predicting antibiotic susceptibility, focusing on potentially rapidly available information, such as lineage or genetic distance from archived isolates. We applied these methods to derive and validate the prediction of antibiotic susceptibility to common antibiotics. We evaluated 968 separate episodes of suspected and confirmed infection with Escherichia coli from three geographically and temporally separated databases in Ontario, Canada, from 2010 to 2018. Across all approaches, model performance (area under the curve [AUC]) ranges for predicting antibiotic susceptibility were the greatest for ciprofloxacin (AUC, 0.76 to 0.97) and the lowest for trimethoprim-sulfamethoxazole (AUC, 0.51 to 0.80). When a model predicted that an isolate was susceptible, the resulting (posttest) probabilities of susceptibility were sufficient to warrant empirical therapy for most antibiotics (mean, 92%). An approach combining multiple models could permit the use of narrower-spectrum oral agents in 2 out of every 3 patients while maintaining high treatment adequacy (∼90%). Methods based on genetic relatedness to archived samples of E. coli could be used to predict antibiotic resistance and improve antibiotic selection.

Common infections in nursing homes: a review of current issues and challenges

Over 1.5 million people live in 16,000 nursing homes in the USA and experience an average of 2 million infections a year. Infections have been associated with high rates of morbidity and mortality, rehospitalization, extended hospital stay and substantial healthcare expenses. Emerging infections and antibiotic-resistant organisms in an institutional environment where there is substantial antimicrobial overuse and the population is older, frailer and sicker, create unique challenges for infection control. This review discusses the common infections, challenges, and a framework for a practical infection prevention program.